Data Engineer II, SPIV Data Engineering

AI in this role

Design and manage data infrastructure, pipelines, and data models to support fraud detection and identity verification.

pythonsqletl

data-modelingdata-pipelinesetldata-governance

Selling Partner Identity Verification is working to identify and prevent fraudulent behavior through all stages of a seller's lifecycle. We are seeking an experienced and highly skilled Data Engineer to join our team and help us build and manage the Data for the data domains within the Selling Partner Identity Verification space. As a Level 5 Data Engineer, you will play a crucial role in designing, implementing, and maintaining our data infrastructure and pipelines. You will work closely with cross-functional teams to deliver scalable and efficient data solutions that drive business value.

Key job responsibilities
You will lead the design and implementation of data pipelines to support the analytical and operational needs of Selling Partner Identity Verification.

You will design and develop scalable, high-performance data systems using modern data platforms and technologies.

You are expected to collaborate with data scientists, analysts, and business stakeholders to understand data requirements and implement appropriate solutions.

Optimize data storage and retrieval systems for improved performance and cost-effectiveness.

Implement data quality checks and monitoring systems to ensure data integrity and reliability.

Mentor junior engineers and provide technical leadership on data engineering best practices and methodologies.

Evaluate and recommend new technologies and tools to enhance our data infrastructure capabilities.

Contribute to the development of data governance policies and procedures.

Troubleshoot and resolve complex data-related issues in production environments.

A day in the life
A Data Engineer II on SPIV starts their day by syncing with the team on data pipeline status, checking for any overnight failures in ETL jobs processing seller identity verification data. They collaborate with data scientists, software engineers, stakeholders to understand new program launches, fraud detection requirements, then design and implement scalable data models using medallion architecture to support these use cases.

About the team
Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship.

Basic qualifications

- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS

Preferred qualifications

- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

How we rate this

Data Engineer II, SPIV Data Engineering at Amazon rates 20 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Data ModelingData PipelinesETLData GovernancePythonSQL

Questions you could be asked

Tell me about a project where data modeling was part of your work. What did you do?
Tell me about a project where data pipelines was part of your work. What did you do?
Tell me about a project where etl was part of your work. What did you do?
Tell me about a project where data governance was part of your work. What did you do?
Walk me through how you've used Python in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Data Modeling, Data Pipelines, ETL, Data Governance, and Python. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.

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